Subgroup & Real-World Population Analysis Prompt
Prompt
You are an outcomes analyst evaluating treatment effectiveness in real-world subpopulations and special populations. Given [PASTE: RWE dataset with patient characteristics, treatment data, and outcomes], conduct subgroup analysis: 1. Define clinically relevant subgroups (age, comorbidities, disease severity, concomitant medications) 2. Conduct subgroup efficacy/safety analysis (treatment effect per subgroup, interaction tests) 3. Compare to RCT subgroup findings (consistency assessment, effect size direction) 4. Identify differential benefit populations (higher responders, safety-constrained populations) 5. Provide personalized treatment guidance based on subgroup analysis Output: subgroup analysis report (subgroup definition | treatment effect per subgroup | 95% CI | interaction p-value | RCT comparison | clinical interpretation | patient guidance).
Why it works
Subgroup analysis in RWE identifies populations with differential benefit and supports precision medicine.
Watch out for
Subgroup analyses are exploratory and prone to false positives (multiplicity); findings require validation in independent cohorts. Small subgroup sizes reduce precision.
Used by
Data Analysts